Visual analysis of large-scale network anomalies

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	Visual analysis of large-scale network anomalies
	Liao, Q. ; Shi, L. ; Wang, C.
刊名	IBM JOURNAL OF RESEARCH AND DEVELOPMENT
	2013
卷号	57 期号:3-4
ISSN号	0018-8646
中文摘要	The amount of information flowing across communication networks has rapidly increased. The highly dynamic and complex networks, represented as large graphs, make the analysis of such networks increasingly challenging. In this paper, we provide a brief overview of several useful visualization techniques for the analysis of spatiotemporal anomalies in large-scale networks. We make use of community-based similarity graphs (CSGs), temporal expansion model graphs (TEMGs), correlation graphs (CGs), high-dimension projection graphs (HDPGs), and topology-preserving compressed graphs (TPCGs). CSG is used to detect anomalies based on community membership changes rather than individual nodes and edges and therefore may be more tolerant to the highly dynamic nature of large networks. TEMG transforms network topologies into directed trees so that efficient search is more likely to be performed for anomalous changes in network behavior and routing topology in large dynamic networks. CG and HDPG are used to examine the complex relationship of data dimensions among graph nodes through transformation in a high-dimensional space. TPCG groups nodes with similar neighbor sets into mega-nodes, thus making graph visualization and analysis more scalable to large networks. All the methods target efficient large-graph anomaly visualization from different perspectives and together provide valuable insights.
英文摘要	The amount of information flowing across communication networks has rapidly increased. The highly dynamic and complex networks, represented as large graphs, make the analysis of such networks increasingly challenging. In this paper, we provide a brief overview of several useful visualization techniques for the analysis of spatiotemporal anomalies in large-scale networks. We make use of community-based similarity graphs (CSGs), temporal expansion model graphs (TEMGs), correlation graphs (CGs), high-dimension projection graphs (HDPGs), and topology-preserving compressed graphs (TPCGs). CSG is used to detect anomalies based on community membership changes rather than individual nodes and edges and therefore may be more tolerant to the highly dynamic nature of large networks. TEMG transforms network topologies into directed trees so that efficient search is more likely to be performed for anomalous changes in network behavior and routing topology in large dynamic networks. CG and HDPG are used to examine the complex relationship of data dimensions among graph nodes through transformation in a high-dimensional space. TPCG groups nodes with similar neighbor sets into mega-nodes, thus making graph visualization and analysis more scalable to large networks. All the methods target efficient large-graph anomaly visualization from different perspectives and together provide valuable insights.
收录类别	SCI
语种	英语
WOS记录号	WOS:000323322800014
公开日期	2014-12-16
内容类型	期刊论文
源URL	[http://ir.iscas.ac.cn/handle/311060/16697]
专题	软件研究所_软件所图书馆_期刊论文
推荐引用方式 GB/T 7714	Liao, Q.,Shi, L.,Wang, C.. Visual analysis of large-scale network anomalies[J]. IBM JOURNAL OF RESEARCH AND DEVELOPMENT,2013,57(3-4).
APA	Liao, Q.,Shi, L.,&Wang, C..(2013).Visual analysis of large-scale network anomalies.IBM JOURNAL OF RESEARCH AND DEVELOPMENT,57(3-4).
MLA	Liao, Q.,et al."Visual analysis of large-scale network anomalies".IBM JOURNAL OF RESEARCH AND DEVELOPMENT 57.3-4(2013).